在行政数据中验证阿片类药物使用障碍诊断:对现有证据和未来方向的评论
Jeffrey F Scherrer1,2,3, Mark D Sullivan4, Marc R LaRochelle5
1Department of Family and Community Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, USA. jeffrey.scherrer@health.slu.edu.
Addiction science & clinical practice
|August 17, 2023
概括
使用医疗记录开发准确的阿片类药物使用障碍 (OUD) 识别算法至关重要. 经过验证的算法将改善对OUD后果,治疗寻求和结果的研究.
科学领域:
- 医疗信息学 医疗信息学
- 临床研究 临床研究
- 公共卫生 公共卫生
背景情况:
- 准确识别阿片类药物使用障碍 (OUD) 对于研究和质量测量至关重要.
- 使用ICD代码的现有行政数据算法在精确测量OUD时存在局限性.
- 需要改进验证方法,以提高电子健康记录中OUD识别的准确性.
研究的目的:
- 要突出有效的阿片类药物使用障碍 (OUD) 识别算法的关键需求.
- 讨论当前OUD诊断码在行政数据中的局限性.
- 探索替代验证方法,以提高OUD算法准确度.
主要方法:
- 对OUD识别算法现有文献的审查.
- 对行政数据的验证方法的批判性评估.
- 讨论初级数据收集和图表抽象作为验证工具.
主要成果:
- 目前的ICD-9和ICD-10代码不足以准确测量OUD.
- 替代验证策略是必要的,以提高算法有效性.
- 结构化诊断面试代表了OUD验证的潜在黄金标准.
结论:
- 有效的OUD识别算法对于推进OUD研究至关重要.
- 改进的算法将改善对OUD后果和治疗结果的研究.
- 准确的OUD识别是对现实世界医疗保健质量测量的关键.
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